- Why bother?
- What is Data Science?
- What are Data Scientists?
- What is Machine Learning?
- What types of problems can Machine Learning solve?
- AI ≠ Machine Learning
- Bias in AI
2023-03-23
[source: trends.google.com]
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
[source: gartner.com]
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
[source: fscj.edu]
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
[source: bdbizviz.com]
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
[source: wikipedia.org]
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
[source: carestruck.org]
[www.oreilly.com/data/free/2017-data-science-salary-survey.csp]
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
[Tom M. Mitchell]
→ Template to describe complex problems with less ambiguity:
→ Preparing decision making program to solve this task is called training
→ Collected email examples are called the training set
→ The program is referred to as a model
(as in a model of the problem of classifying spam from non-spam)
Machine Learning tasks T typically classified into two broad categories:
Supervised learning:
The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
Unsupervised learning:
No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
AI is the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals
[Poole, Mackworth, Goebel (1998). Computational Intelligence: A Logical Approach]
Machine learning is a subset of artificial intelligence that is concerned with the construction and study of systems that can learn from data.
[source: www.oreilly.com/data/free/2017-data-science-salary-survey.csp]
Goal: Find frequent/interesting patterns, associations, correlations among sets of items in a transactional database
[source: towardsdatascience.com]
[source: xkcd.com]
Consumers of AI predictions (i.e. businesses) need adequate understanding of model limitations and proper interpretation
Good article – Bias and ethical considerations in machine learning and the automation of risk assessment